Volume 23 Issue 5
Oct.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(5): 223-233
LI Bin, ZHU Peng-peng, XIAO Run-mou, LI Zi-tian, JIN Yin-li. Application of combined empowerment cloud model to expressway channel adaptability assessments[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 223-233. doi: 10.19818/j.cnki.1671-1637.2023.05.015
Citation: LI Bin, ZHU Peng-peng, XIAO Run-mou, LI Zi-tian, JIN Yin-li. Application of combined empowerment cloud model to expressway channel adaptability assessments[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 223-233. doi: 10.19818/j.cnki.1671-1637.2023.05.015
shu

Application of combined empowerment cloud model to expressway channel adaptability assessments

doi: 10.19818/j.cnki.1671-1637.2023.05.015
  • 1.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Transport Planning and Research Institute of Ministry of Transport, Beijing 100028, China

  • 3.

    School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2022YFE0102700

National Key Research and Development Program of China 2019YFB1600700

Ministry of Transportation and Communications Strategic Planning Policy Project 20222104

Fundamental Research Funds for the Central Universities 300102223208

More Information
  • Author Bio:

    LI Bin(1982-), male, associate professor, PhD, binli@chd.edu.cn

    ZHU Peng-peng(1994-), male, assistant engineer, zhupp@tpri.org.cn

  • Received Date: 2023-05-19
    Available Online: 2023-11-17
  • Publish Date: 2023-10-25
    Abstract
  • In order to scientifically assessment the adaptability between expressway channels and regional development, the influencing factors of expressway channels on the regional economy were analyzed. The evaluation index screening method of the anti-image correlation matrix was used for index dimensionality reduction, and the evaluation index system of expressway adaptability of regional channels was established. The weights of each evaluation index of the model were calculated by the objective assignment through the gray correlation, and the errors caused by the subjective judgment of the analytic hierarchy process (AHP) were compensated in the algorithm. A cloud model with a hybrid AHP-gray correlation degree constrained cone was developed, and 46 road sections of the Shenhai Expressway channel in 2019 were evaluated. Relevant improvement opinions and specific measures were proposed to address the problems of the non-adaptive road sections in terms of capacity expansion and reconstruction, differentiated charging, and active traffic management. Research results show that the model complexity can reduces substantially without affecting the model accuracy by reducing the expressway adaptability evaluation indexes from 18 to 9. By evaluating the Shenhai Expressway, the moderately over-adapted road sections, the overall adaptive road sections, the preliminary adaptive road sections, the relative lagging road sections and the seriously constrained road sections account for 6.52%, 21.74%, 43.48%, 21.74% and 6.52%, respectively. Compared with the traditional data enveloping analysis (DEA) model, the cloud model with AHP-grey correlation degree constraint cone has 17.39% fewer moderately over-adapted road sections, 43.48% more preliminary adaptive road sections, and 6.52% more seriously constrained road sections. In particular, the degree of maladaptation is the most serious in the inner sections of Fuzhou City, Quanzhou City, Jinjiang City, and Xiamen City, which are not evaluated by the traditional DEA model. It can be seen that the accuracy and scientificity of the evaluation results can be improved by the cloud model using the AHP-gray correlation degree combination assignment, and it is an effective method to solve the problem of expressway channel adaptability assessment.

     

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